Table REF  lists quantitative comparison between PanoDepth and other state-of-the-art omnidirectional monocular depth estimation methods [1]}, [2]}, [3]}, [4]} on both Stanford2D3D [5]} and 360D [2]} datasets.
As shown in Table REF , our method is able to reduce Abs Rel error by 19.60% on Stanford2D3D and 25.85% on 360D compare to the current leading 360 monocular depth estimation approach BiFuse [3]}. Note that BiFuse [3]} uses a network architecture with more than 200M parameters and has a large computation complexity for sharing information between CubeMap [9]} and ERP formats. The framework with distortion-aware module proposed in [10]} outperforms ours but it has more than 60M parameters.
Our framework has only around 16M parameters with a smaller computation overhead. Comparing the performance with ODE-CNN [4]} on 360D, our approach achieves comparable results while ODE-CNN requires additional depth sensor input.
Figure REF  shows the qualitative comparison with the state-of-the-art approaches. As we can see, our method generates high-quality depth with a detailed surface, sharp edges, and precise range.
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